One of the most important features of xarray is the ability to convert to and
from pandas objects to interact with the rest of the PyData
ecosystem. For example, for plotting labeled data, we highly recommend
using the visualization built in to pandas itself or provided by the pandas
aware libraries such as Seaborn.

We see that each variable and coordinate in the Dataset is now a column in the
DataFrame, with the exception of indexes which are in the index.
To convert the DataFrame to any other convenient representation,
use DataFrame methods like reset_index(),
stack() and unstack().

Notice that that dimensions of variables in the Dataset have now
expanded after the round-trip conversion to a DataFrame. This is because
every object in a DataFrame must have the same indices, so we need to
broadcast the data of each array to the full size of the new MultiIndex.

Likewise, all the coordinates (other than indexes) ended up as variables,
because pandas does not distinguish non-index coordinates.

DataArray objects have a complementary representation in terms of a
pandas.Series. Using a Series preserves the Dataset to
DataArray relationship, because DataFrames are dict-like containers
of Series. The methods are very similar to those for working with
DataFrames: